Scalar Field

What do they actually do

Scalar Field is a web-based trading research terminal that uses AI agents to help traders and analysts run backtests, chain multi-step research, and keep a reproducible research trail. Today it exposes datasets across equities, options, futures, fundamentals, SEC filings, insider and congressional trades, economic calendars, and macro data (including FRED), and lets users run query-driven analysis with defined compute limits pricing homepage.

The Pro plan is $79/month with a 15‑day trial and includes 300 queries/month, up to 20 minutes of compute per query, 10 Scalar Agents (automations and research bots), 5 Custom News Agents, and connectors for Capital IQ, Robinhood, and Interactive Brokers. Enterprise adds SSO/SAML, SOC 2 posture, private/VPC deployment, team workspaces, more data sources, and expanded connectors pricing.

Who are their target customer(s)

• Active independent/retail traders paying for advanced tools: Terminals and scattered data sources make it slow to test an idea end‑to‑end; they struggle to run compute‑heavy backtests, chain signals, and keep a reproducible research trail in one place. Scalar’s Pro plan targets this segment at $79/month YC company pricing.
• Quant researchers and algo traders at prop shops or hedge funds: They need fast backtests across many datasets and programmatic workflows. Existing terminals or internal stacks either lack the compute/automation for large backtests and agent-driven workflows or require bespoke infra to trigger logic and connect data/brokers YC company.
• Buy‑side analysts and portfolio managers at asset managers/RIAs: Validating ideas across fundamentals, flows, earnings and macro requires stitching charts, news, and filings manually, losing context between sessions and slowing the handoff from research to orders YC company.
• Trading‑ops/integration engineers supporting trading desks: Connecting data feeds, brokers, and compute safely is heavy; they need SSO, SOC2/compliance options, private deployments, and reliable automation instead of brittle point integrations pricing.
• Macro researchers and event‑driven strategists: Reproducible backtests that align with historic economic release timing and tying those signals into market‑level trades are hard with standard terminals. Scalar highlights broad macro/FRED coverage to address this homepage.

How would they acquire their first 10, 50, and 100 customers

• First 10: Founder‑led, hands‑on pilots with active retail traders, quant contacts, and community leaders; offer free pilots, white‑glove onboarding, and small custom integrations to deliver a full idea→execution loop quickly, then convert to Pro or early‑adopter discounts while capturing detailed feedback and session logs YC company pricing.
• First 50: Publish 5–10 ready‑made templates and walkthroughs; run workshops in trader Discords/Telegram/forums; offer short‑term credits for referrals. In parallel, pitch small prop/quant groups with a 30–90 day pilot and a standardized broker/connectivity checklist to reduce friction pricing.
• First 100: Productize pilots into a self‑serve paid tier with clear trial→paid funnels and strategy templates while pursuing paid pilots with mid‑size props and RIAs for enterprise features (SSO, private deployments). Add broker/data/education channel partnerships and use reproducibility case studies in procurement.

What is the rough total addressable market

Top-down context:

Scalar Field sells into both retail traders who pay for research tools and professional desks that buy terminals and analytics, a category that spans mass‑market tools like TradingView and institutional terminals like Bloomberg TradingView Bloomberg Terminal.

Bottom-up calculation:

Retail: assume 200,000 global power users willing to pay ~$80/month for agent‑driven research/backtests → ~$192M/year. Professional: assume 100,000 seats at smaller funds/prop/RIAs at ~$3,000/year → ~$300M/year. Indicative blended TAM ≈ ~$500M/year for Scalar’s initial target segments.

Assumptions:

• Retail power users are a small fraction of active traders and are willing to pay ~$80/month for advanced research automation.
• Professional buyers outside top‑tier terminals (e.g., prop shops, mid‑size funds, RIAs) value agent/backtest/reproducibility and can spend ~$3k/seat/year.
• Estimates exclude very large enterprise terminal spend and non‑equities specialized markets, making this a conservative, initial TAM.

Who are some of their notable competitors

• QuantConnect: Cloud algorithmic platform to code, backtest, and (optionally) live‑trade across shared datasets and broker connectors; powerful for code‑centric teams but you assemble multi‑step workflows and research context yourself pricing/docs docs.
• QuantRocket: Python/Jupyter research stack packaging data, scheduled jobs, and backtests; infra‑heavier (you run/maintain it) and not aimed at GUI‑first or non‑technical retail users docs.
• TradingView: Popular retail charting and idea‑sharing terminal with Pine Script and simple backtests; great for visual research but not built for large compute‑heavy experiments or production automation/reproducible multi‑step trails site Pine review.
• Bloomberg Terminal: Institutional terminal bundling real‑time market data, news, analytics and order routing; broad asset coverage but expensive and oriented to human workflows rather than lightweight, agent‑driven automation product overview.
• Koyfin: Dashboard research tool focused on fundamentals, macro dashboards, and portfolio reporting; speeds manual analysis but lacks programmatic backtesting, broker automation, or agent workflow chaining features.